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outlines/models/transformers.py

@@ -58,18 +58,22 @@ class CodeLlamaTokenizerFast:  # type: ignore
 class TransformerTokenizer(Tokenizer):
     """Represents a tokenizer for models in the `transformers` library."""
 
-    def __init__(self, tokenizer_or_model_name: Union["PreTrainedTokenizerBase", str], **kwargs):
+    def __init__(
+        self, tokenizer_or_model_name: Union["PreTrainedTokenizerBase", str], **kwargs
+    ):
         if isinstance(tokenizer_or_model_name, str):
             from transformers import AutoTokenizer
 
             kwargs.setdefault("padding_side", "left")
             self.model_name = tokenizer_or_model_name
             # TODO: Do something to make this hashable?
             self.kwargs = kwargs
-            self.tokenizer = AutoTokenizer.from_pretrained(tokenizer_or_model_name, **kwargs)
+            self.tokenizer = AutoTokenizer.from_pretrained(
+                tokenizer_or_model_name, **kwargs
+            )
         else:
             self.tokenizer = tokenizer_or_model_name
-        
+
         self.eos_token_id = self.tokenizer.eos_token_id
         self.eos_token = self.tokenizer.eos_token
 
@@ -112,7 +116,9 @@ def convert_token_to_string(self, token: str) -> str:
     def __eq__(self, other):
         if isinstance(other, type(self)):
             if hasattr(self, "model_name") and hasattr(self, "kwargs"):
-                return other.model_name == self.model_name and other.kwargs == self.kwargs
+                return (
+                    other.model_name == self.model_name and other.kwargs == self.kwargs
+                )
             else:
                 return other.tokenizer == self.tokenizer
         return NotImplemented

tests/generate/test_integration_transformers.py

@@ -618,7 +618,7 @@ def __call__(
 
 
 def test_transformers_use_existing_model_and_tokenizer():
-    from transformers import AutoTokenizer, AutoModelForCausalLM
+    from transformers import AutoModelForCausalLM, AutoTokenizer
 
     rng = torch.Generator()
     rng.manual_seed(10000)